DNNs are already being applied successfully for the offline analysis of data recorded by existing high energy physics experiments [1]}, including operating LArTPCs. In the case of the latter, MicroBooNE is pioneering the use of deep learning for neutrino physics analyses (see, e.g., [2]}, [3]}), and similar DNN-based methodologies have now been adopted for several analyses planned with the future DUNE experiment [4]}. Machine learning approaches to LArTPC data analysis are gaining increasing traction (see, e.g. [5]}); meanwhile, new techniques are continually being considered to improve data processing latency and resource requirements, with promising results [6]}.
